Field of Invention
The present invention relates generally to the field of computed tomography (CT). More specifically, the present invention is related to view point recognition in CT images.
Discussion of Related Art
Coronary heart disease is the most common cause of mortality in the United States and contributes to one in every five deaths, according to the American Heart Association. Acute coronary symptoms result in hospitalization of nearly 900,000 Americans every year. Cardiac catheterization under CT or X-ray angiography provides definitive evidence for plaque build-up in coronary arteries. However, the invasive nature of such procedures prohibits their use for screening purposes in low to intermediate risk individuals. This has created a growing interest in cardiac computed tomography (CT) as an imaging technology to study the heart vessels and chambers for screening purposes. Several studies have shown that cardiac CT, without the use of a contrast agent, has a very high specificity and provides a negative predictive value of nearly 100% and can be used to rule out a large number of low and intermediate risk patients without the need for invasive methods (see for example, the paper to Budoff et al. titled “Assessment of Coronary Artery Disease by Cardiac Computed Tomography,” Circulation 114, 2006, pp. 1761-1791).
The effective and wide-spread use of CT as a screening methodology for cardiovascular disease could be facilitated by the introduction of an end-to-end cardiology/radiology “cognitive assistant”. A cognitive assistant is a software system with the ability to automatically complete the pre-processing steps, recognize or generate the appropriate views within a complete scan, extract relevant features and concepts from an image and the text associated with the image, run image analysis methods to extract relevant features, and generate a clinically relevant outcome, such as the calcium score or likelihood of disease. These kinds of systems have the ability to reduce the workload, prevent errors, and enable population screening. As an example, previous work (see, for example, the paper to Syeda-Mahmood et al. titled “Aalim: Multimodal Mining for Cardiac Decision Support,” Computers in Cardiology, vol 34, 2007, pp. 209-212) has reported a decision support system for cardiology that derives the consensus opinions of other physicians who have looked at similar cases. Such a decision support system generates a report that summarizes possible diagnoses based on statistics from similar cases. In deploying a system of this type, one needs to retrieve the relevant or similar images and activate the image analytics processes that are often dependent on the modality and viewpoint of the image.
In cardiac imaging, the viewpoint of the image is an essential input for any algorithm designed to measure clinical features of the heart, such as detection of left ventricle, valves, thickness of the pericardium, etc. Since viewpoint recognition is often the first step in the analytic pipeline within a cognitive assistant system, a nearly perfect classification accuracy is needed. Even though DICOM headers (based on the Digital Imaging and Communications in Medicine standard) provide optional tags to store modality information, viewpoint is often not recorded. Also, as several investigators have reported (see, for example, the paper to Gueld et al. titled “Quality of DICOM Header Information for Image Categorization,” SPIE Medical Imaging, vol. Proc. SPIE 4685, 2002, pp. 280-287), one cannot rely on the accuracy and completeness of DICOM headers for image categorization particularly on optional and manually entered tags (see, for example, the paper to Yoshimura et al. titled “Operating Data and Unsolved Problems of the DICOM Modality Worklist: An Indispensable Tool in an Electronic Archiving Environment,” Radiation Medicin, v21(2), 2003, pp. 68-73). The introduction of a machine learning approach to slice/viewpoint recognition could also facilitate the use of 2D technics in segmentation and anatomy recognition within the cognitive assistant system, providing savings in terms of computational resources compared to 3D.
Much of the previous work in cardiac viewpoint detection focuses on echocardiography images (see, for example, the paper to Park et al. titled “Automatic Cardiac View Classification of Echocardiogram,” ICCV, 2007, pp. 1-8, and the paper to Kumar et al. titled “Echocardiogram View Classification Using Edge Filtered Scale-Invariant Motion Features,” IEEE CVPR, 2009, pp. 723-730). Due to the small field of view, the free-hand nature of ultrasound images, and the fundamentally different nature of ultrasound image texture, the methods cannot be directly applied to CT imaging.
Embodiments of the present invention are an improvement over prior art systems and methods.